1. Field of the Invention
This invention generally relates to digital image compression and, more particularly, to a system and method for using a global motion predictor (GMP) to reduce the computations associated with encoding video frames, and to improve video encoding quality.
2. Description of the Related Art
Motion estimation is the main bottleneck in most conventional video-encoding systems. Therefore, many fast motion estimation methods have been developed for real-time video encoding. Predictive motion estimation techniques have become widely accepted due to their low complexity, as compared to the brute force full search method, and they have better performance than other fast motion search methods. The efficiency of the predictive motion search methods comes from the continuity of the motion vector fields. Typically, several highly likely motion vector candidates or predictors can be chosen from the immediately adjacent image blocks in the same frame, and from the corresponding image blocks in the neighboring frames in the temporal domain. One candidate is then selected from the predictors based on a certain measurement. A local search is performed around the best candidate to refine the final motion vector.
Motions in video images are typically either regarded as object motions (also called local motions) or camera motions (also called global motions). Most conventional predictive motion estimation algorithms focus mainly on the local motions, due to the consideration of the motion predictions from only the neighboring image blocks (either spatially or temporally).
It would be advantageous if global motion parameters could be used to simplify the motion estimates needed to encode (compress) video frames.
The global motion in an image sequence is usually considered as the relative motion of the camera with respect to the image background. There are a number of global motion modeling methods, which consider some or all of panning, zooming, rotation, affine motions, and perspective motions. Mathematically, these global operations can be described as different transform matrices. However, in the discrete digital image domain, it is usually computationally quite expensive to solve the global motion parameters strictly following the mathematical models, which are well defined for the continuous space.
Some techniques have been developed to conduct global motion estimation using a motion vector field obtained by a local motion estimation algorithm. Global motion parameters can then be derived based on the mathematical models. The complexity of local motion estimation is a computational barrier for practical usages. In another technique, hardware sensors are mounted within a video camera to catch the camera motion. The hardware implementation might be suitable for high-end video camera and coding systems, however, it is very costly for regular consumer electronics.
A difficulty in global motion estimation is the existence of independently moving objects that introduce bias to the estimated motion parameters. Many methods have been proposed in the past years to gain the efficiency and robustness for global motion estimation. One of the latest techniques uses video object masks to remove the moving objects in order to obtain higher robustness. The overhead needed to create video object segmentations is difficult to obtain in most video systems.
Another global motion estimation technique uses a truncated quadratic function to define the error criterion, in order to remove the image pixels of moving objects. This method significantly improves the robustness and efficiency. However, the truncation utilizes a pre-fixed threshold, which is not well defined.
One common aspect of the software-based global motion estimation methods mentioned above is that they derive their global motion parameters from a comparison of two temporally consecutive image frames, using the full content in the images. This is a computationally expensive process.
It would be advantageous if a computationally simple process could be used for deriving global motion predictors, from global motion parameters, that could be applied to the above-mentioned motion estimates.